Add to ORKGThe quality of training examples is one of the most important factors for effec-tive and efficient training of neural networks, provided the network architecture and the weight modification rule are fixed. Earlier work [4] shows that the per-ceptron learning can be significantly accelerated by utilizing specific instances
Teacher neural networks are a systematic experimental approach to study neural networks. A teacher i...
When learning a new concept, not all training exam-ples may prove equally useful for training: some ...
this paper (Parisi, Nolfi, & Cecconi, 1992). The performance of the elite did not improve when l...
Inductive Inference Learning can be described in terms of finding a good approximation to some unkno...
Existing metrics for the learning performance of feed-forward neural networks do not provide a satis...
In this work, we study how the selection of examples affects the learn-ing procedure in a boolean ne...
The quality and size of the training data sets is a critical stage on the ability of the artificial ...
Learning curves show how a neural network is improved as the number of training examples increases a...
In the last decade, motivated by the success of Deep Learning, the scientific community proposed sev...
In this work, the network complexity should be reduced with a concomitant reduction in the number of...
When humans learn a new concept, they might ignore examples that they cannot make sense of at first,...
Performance metrics are a driving force in many fields of work today. The field of constructive neur...
Neural networks are widely applied in research and industry. However, their broader application is h...
Abstract—Training Artificial Neural Networks (ANN) is relatively slow compared to many other machine...
When a large feedforward neural network is trained on a small training set, it typically performs po...
Teacher neural networks are a systematic experimental approach to study neural networks. A teacher i...
When learning a new concept, not all training exam-ples may prove equally useful for training: some ...
this paper (Parisi, Nolfi, & Cecconi, 1992). The performance of the elite did not improve when l...
Inductive Inference Learning can be described in terms of finding a good approximation to some unkno...
Existing metrics for the learning performance of feed-forward neural networks do not provide a satis...
In this work, we study how the selection of examples affects the learn-ing procedure in a boolean ne...
The quality and size of the training data sets is a critical stage on the ability of the artificial ...
Learning curves show how a neural network is improved as the number of training examples increases a...
In the last decade, motivated by the success of Deep Learning, the scientific community proposed sev...
In this work, the network complexity should be reduced with a concomitant reduction in the number of...
When humans learn a new concept, they might ignore examples that they cannot make sense of at first,...
Performance metrics are a driving force in many fields of work today. The field of constructive neur...
Neural networks are widely applied in research and industry. However, their broader application is h...
Abstract—Training Artificial Neural Networks (ANN) is relatively slow compared to many other machine...
When a large feedforward neural network is trained on a small training set, it typically performs po...
Teacher neural networks are a systematic experimental approach to study neural networks. A teacher i...
When learning a new concept, not all training exam-ples may prove equally useful for training: some ...
this paper (Parisi, Nolfi, & Cecconi, 1992). The performance of the elite did not improve when l...